Glaucoma is an optic neuropathy resulting in characteristic visual field defects. It arises from progressive damage to the optic nerve (ON) and retinal ganglion cells (RGCs) and their axons, the retinal nerve fiber layer (RNFL). Investigating the relationship between development of functional damage in the visual field and structural glaucomatous changes of the RNFL has been the purpose of numerous studies [1-5].
Diagnostic instruments providing quantitative analyses in glaucoma assess either structural or functional aspects of the disease. Optical Coherence Tomography (OCT) is one technique capable of imaging the retina and providing quantitative analysis of RNFL measurements and measuring the optic nerve head. OCT is a noninvasive interferometric technique that provides cross sectional images and thickness measurements of the RNFL (RNFLT) with high resolution [6] and good reproducibility [7-9]. Standard Automated white-on-white Preemptory (SAP) is the standard for assessing visual function by examination of the visual field. Parametric tests are able to provide quantitative measurements of differential light sensitivity at many test point locations in the visual field, and commercially available statistical analysis packages help clinicians in identifying significant visual field loss [10]. The diagnostic performance of both OCT and SAP in glaucoma as well as the correlation between SAP and OCT measurements has been investigated [11-14].
Clinical studies suggest that these diagnostic tests, used in isolation, provide useful information on the diagnosis and progression of the disease and, used in conjunction, provide supportive and complementing information which could lead to improved accuracy in disease detection and monitoring of progression. However, there is not one single diagnostic test used in isolation that provides adequate diagnostic accuracy and applicability across patient populations and disease dynamic range. Multi-modality testing is desired to improve applicability and accuracy. In practice, clinicians are often expected to correlate results from different tests to make a clinical assessment regarding diagnosis and/or progression, usually, based on subjective visual review of multiple reports. Such a task is difficult and subjective, and highly variable across observers.
It is conceivable that integration of functional and structural test measurements could provide more relevant information and thus improved diagnostic performance for classification systems when used as input data. The relevance of integrated diagnostic information is dependent on the underlying relationship between structural and functional measurements. Statistical approaches such as the linear model constructed by Hood et al related RNFLT values to sensitivity losses in SAP [15]. Other studies trying to map the individual visual field test points in SAP to areas of the peripapillary RNFL through different models, showed moderate correlations between visual field sensitivity values and structural measurements [16,17]. More recent attempts to model the function—structure relationship in glaucoma demonstrated that machine learning algorithms, such as radial basis function artificial neural networks (ANNs), improved the modeling accuracy compared to linear methods [18]. Recently Boland et al proposed a structure function index (SFI) as a model to unify retinal ganglion cell structure and function based on knowledge of retinal nerve fiber layer anatomy [41]. The SFI function is the multiplication of 3 probabilities: the probability of disease at a point in the visual field with the probability of abnormality on HRT (Heidelberg Retina Tomograph, Heidelberg Engineering, a confocal scanning laser opthalmoscope that generates and analyzes topographic images of the retina) sector and the probability that these measurements are correlated. These probabilities were derived from glaucoma suspects and not a normal population.
The use of machine learning classifiers (MLCs) in glaucoma diagnosis using either functional or structural measures has been previously explored [19]. MLCs like ANNs have been used for classification of tests based on structural or functional measurements [20-27] and for detection of glaucoma progression [28,29]. ANN-based classification demonstrated better accuracy than linear methods [22, 23, 30] and performed at least as well as human experts [31].
A few studies have examined the diagnostic performance of combining functional and structural data with MLCs for glaucoma diagnosis [32, 33]. One of the main advantages of MLCs is their ability to learn a classification task by training on given examples. Such adaptive classification based on the available data is useful, since a complete analytic theory of the structure-function relationship in glaucoma does not yet exist. The performance of MLCs can be influenced by a number of factors including data selection bias, choice of input and classifier architecture.
In light of the above, there is a need for improved systems and methods for diagnosing and analyzing the progression of glaucoma based on the combination of data from structural and functional measurements and machine learning classification.